Author Age Prediction from Text Using Linear Regression

While the study of the connection between discourse patterns and personal identification is decades old, the study of these patterns us-ing language technologies is relatively recent. In that more recent tradition we frame author age prediction from text as a regression prob-lem. We explore the same task using three very different genres of data simultaneously: blogs, telephone conversations, and online fo-rum posts. We employ a technique from do-main adaptation that allows us to train a joint model involving all three corpora together as well as separately and analyze differences in predictive features across joint and corpus-specific aspects of the model. Effective fea-tures include both stylistic ones (such as POS patterns) as well as content oriented ones. Us-ing a linear regression model based on shallow text features, we obtain correlations up to 0.74 and mean absolute errors between 4.1 and 6.8 years.